Valuing the field : child welfare in an international context
Bibliographic record
Abstract
Part 1 Listening to messages from the first line: child welfare on the eve of the 20th century - what we have learned, Sven Hessle changing the face of child welfare - perspectives from the field, Joan Gilroy efforts at empowering youth - Youth-In-Care and the Youth-In-Care networks in Ontario and Canada, Susan Strega. Part 2 Building family and community supports: the focus on family when children are at risk - Swedish policy in practice, Sven Hessle et al the Wraparound process - strength-based practice, Ralph Brown and Andrew Debicki from case and client to citizen - an innovation in child welfare, Brian Wharf, Riley Hern and Judy Burgess. Part 3 Children on the move: unaccompanied and asylum-seeking children encounter Sweden, Marie Hessle offering relief to unaccompanied asylum seekers in Holland, Yyvonne Aronson et al. Part 4 Valuing diversity in child welfare communities: tackling racism in everyday realities - a task for social workers, Lena Dominelli a first nations' experience in first nations child welfare services, Audrey Hill it takes a village - building networks of support for African Nova Scotian families and children, Wanda Thomas Bernard and Candace Bernard. Part 5 Valuing the field in social work education: developing partnerships in social work education in Britain, Sally Richards et al. Part 6 Conclusion: valuing the field - lessons from innovation, Marilyn Callahan.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.034 |
| Scholarly communication | 0.024 | 0.017 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".